论文标题
使用深层上下文化的单词嵌入的隐喻检测
Metaphor Detection using Deep Contextualized Word Embeddings
论文作者
论文摘要
隐喻在自然语言中无处不在，它们的检测在许多自然语言处理任务中起着至关重要的作用，例如语言理解，情感分析等。大多数现有的隐喻检测方法都取决于复杂，手工制作和精细调整的功能管道，从而极大地限制了其适用性。在这项工作中，我们提出了一种由深层上下文化的单词嵌入，双向LSTM和多头注意机制组成的端到端方法，以解决自动隐喻检测的任务。与许多其他现有方法不同，我们的方法仅需要原始文本序列作为输入特征来检测短语的隐喻性。我们将方法的性能与两个基准数据集（Trofi和MOH-X）上的现有基准进行比较。实验评估证实了我们方法的有效性。
Metaphors are ubiquitous in natural language, and their detection plays an essential role in many natural language processing tasks, such as language understanding, sentiment analysis, etc. Most existing approaches for metaphor detection rely on complex, hand-crafted and fine-tuned feature pipelines, which greatly limit their applicability. In this work, we present an end-to-end method composed of deep contextualized word embeddings, bidirectional LSTMs and multi-head attention mechanism to address the task of automatic metaphor detection. Our method, unlike many other existing approaches, requires only the raw text sequences as input features to detect the metaphoricity of a phrase. We compare the performance of our method against the existing baselines on two benchmark datasets, TroFi, and MOH-X respectively. Experimental evaluations confirm the effectiveness of our approach.
